Clinical VMAT machine parameter optimization for localized prostate cancer using deep reinforcement learning.

Background: Volumetric modulated arc therapy (VMAT) machine parameter optimization (MPO) remains computationally expensive and sensitive to input dose objectives creating challenges for manual and automatic planning. Reinforcement learning (RL) involves machine learning through extensive trial-and-error, demonstrating performance exceeding humans, and existing algorithms in several domains.

Purpose: To develop and evaluate an RL approach for VMAT MPO for localized prostate cancer to rapidly and automatically generate deliverable VMAT plans for a clinical linear accelerator (linac) and compare resultant dosimetry to clinical plans.

Deep learning segmentation of organs-at-risk with integration into clinical workflow for pediatric brain radiotherapy.

Purpose: Radiation therapy (RT) of pediatric brain cancer is known to be associated with long-term neurocognitive deficits. Although target and organs-at-risk (OARs) are contoured as part of treatment planning, other structures linked to cognitive functions are often not included. This paper introduces a novel automatic segmentation tool specifically designed for the unique challenges posed by pediatric patients undergoing brain RT, as well as its seamless integration into the existing clinical workflow.

Surgical navigation for guidewire placement from intraoperative fluoroscopy in orthopaedic surgery.

. Surgical guidewires are commonly used in placing fixation implants to stabilize fractures. Accurate positioning of these instruments is challenged by difficulties in 3D reckoning from 2D fluoroscopy.

Factors that influence patient and public adverse drug reaction reporting: a systematic review using the theoretical domains framework.

Background: Only 5-10% of all adverse drug reactions (ADRs) are reported. Mechanisms to support patient and public reporting offer numerous advantages to health care systems including increasing reporting rate. Theory-informed insights into the factors implicated in patient and public underreporting are likely to offer valuable opportunity for the development of effective reporting-interventions and optimization of existing systems.